Olswold, Curtis L.

3610 Background: Chronic lower grade adverse events (AE) can negatively affect a patient’s quality of life but it is difficult to capture using a traditional toxicity reporting approach. A novel AE reporting method was recently developed to describe, summarize, and present longitudinal AE profiles(Lopes et al, 2021). We leveraged this method to describe and compare the AE profiles of doublet chemotherapy (DC) + Cetuximab and DC alone in mCRC patients. Methods: This AE reporting method utilizes two additional AE metrics to complement the maximum (max) toxicity grade usually reported in clinical trials. Onset time indicates the time period in which max grade for an AE occurred for the first time, defined here as “early” (i.e. within first 42 days) and “late” (i.e. after the 42nd day). AE Load (AEL) indicates the overall severity of an AE in the entire treatment. AEL varies from 0 to 1. Higher AEL indicates a worse overall severity of that AE over time. AEL is the key metric for describing chronic AE. We included patients receiving DC + cetuximab (n = 738) and DC alone (n = 564 [ref group]) from two randomized first-line trials in the ARCAD database. Diarrhea, rash, hand-foot syndrome (HFS), fatigue, anorexia, and mucositis were examined and adjusted for backbone (FOLFOX vs. FOLFIRI), ECOG PS, sex, site location, dose reduction, and treatment length. Results: For rash, DC + cetuximab had a higher risk of G3+ (21% vs. 0.5%; odds ratio {OR} [95% confidence interval {CI}] = 50 [16,157], p < 0.001), increased overall severity over the entire treatment (AEL = 0.257 vs. 0.069; Adjusted difference in means–Mdiff [95% CI] = 0.22 [0.21,0.23], p < 0.001), and increased risk of early onset (67% vs. 33%; OR [95% CI] = 4.3 [2.7,6.7], p < 0.001). DC + cetuximab also had higher AEL for rash across max grades (p < 0.001 within G1, G2, and G3+). For HFS, DC + cetuximab had a higher risk of G3+ (OR [95% CI] = 6.0 [2.5,14], p < 0.001), increased overall severity (AEL = 0.139 vs. 0.087; Mdiff [95% CI] = 0.03 [0.03,0.04], p < 0.001), and slightly earlier onset (12.4 vs. 13.9 weeks; Mdiff, weeks [95% CI] = -4.9 [-0.80,-9.0], p = 0.021). Within each max grade, DC + cetuximab did not have higher AEL of HFS. No associations were found for diarrhea, fatigue, anorexia, or mucositis. Conclusions: The addition of cetuximab is associated with higher grade, more persistent, and more immediate rash. The higher severity in HFS with the addition of cetuximab appears to be related to higher grade but not chronic HFS. This method may be useful to describe different strategies, e.g. intermittent cetuximab. It provided a comprehensive view of acute and chronic toxicity profiles supporting its potential interest as new metrics in clinical trials. Lopes GS, Tournigand C, Olswold CL, et al. Adverse event load, onset, and maximum grade: A novel method of reporting adverse events in cancer clinical trials. Clinical Trials. 2021;18(1):51-60

Background Current adverse event reporting practices do not document longitudinal characteristics of adverse effects, and alternative methods are not easily interpretable and have not been employed by clinical trials. Introducing time parameters in the evaluation of safety that are comprehensive yet easily interpretable could allow for a better understanding of treatment quality. In this study, we developed and applied a novel adverse event reporting method based on longitudinal adverse event changes to aid describing, summarizing, and presenting adverse event profile. We termed it the “Adverse Event Load, Onset, and Maximum Grade” method. Methods We developed two adverse event summary metrics to complement the traditional maximum grade report. Onset time indicates the time period in which the maximum grade for a specific adverse event occurred and was defined as “early” (i.e. maximum grade happened for the first time before 6 weeks) or “late” (i.e. after the 6 th week). Adverse event load indicates the overall severity of a specific adverse event over the entire treatment. Higher adverse event load indicates a worse overall experience. These metrics can be calculated for adverse events with different maximum grades, in treatments with planned changes (e.g. dosage changes), using data sets with different number of adverse event data points between treatments (e.g. treatments with longer cycle lengths may have less adverse event data points) and on data sets with different adverse event data availability (e.g. cycle basis and patient-outcome reports). We tested the utility of this method using individual patient data from two major backbone therapies (“Irinotecan” and “Oxaliplatin”) from the N9741 trial available in the Fondation ARCAD database ( fondationarcad.org ). We investigated profiles of diarrhea, neutropenia/leukopenia, and nausea/vomiting. Results Our method provided additional information compared to traditional adverse event reports. For example, for nausea/vomiting, while patients in Irinotecan had a higher risk of experiencing maximum grade 3–4 (15.6% vs 7.6%, respectively; p < 0.001), patients in both groups experienced similar severity over time (adverse effect load = 0.102 and 0.096, respectively; p = 0.26), suggesting that patients in Oxaliplatin experienced a lower-grade but more persistent nausea/vomiting. For neutropenia/leukopenia, more patients in Irinotecan experienced their maximum grade for the first time early in the treatment compared to patients in Oxaliplatin (67.9% vs 41.7%; p < 0.001), regardless of maximum grade. Longitudinal information can help compare treatments or guide clinicians on choosing appropriate interventions for low-grade but persistent adverse event or early adverse event onset. Conclusion We developed an adverse event reporting method that provides clinically relevant information about treatment toxicity by incorporating two longitudinal adverse event metrics to the traditional maximum grade approach. Future research should establish clinical benchmarks for metrics included in this adverse event reporting method.

89 Background: Monitoring of adverse events (AE) is crucial in clinical trials. Maximum grade per patient (pt) is commonly used but better metrics are needed to aid describing and comparing AE profiles. Methods: We developed and evaluated 2 longitudinal AE summary metrics. 1) Onset time of max grade refers to whether a pt has max grade within pre-specified timeframes for an AE, e.g. onset can be defined as “early” (max grade before the 6th cy) or “late” (≥ 6th cy). 2) Adverse effect load (AEL) is a longitudinal score of worst grade possible excluding death over the entire treatment for an AE. Higher AEL means worst overall experience of AE. We applied these metrics to real trial data. Pts were from 6 mCRC trials included in ARCAD database and received Irinotecan-based (n = 1,119) or Oxaliplatin-based (n = 1,230) treatments that did not include biologicals. AEs were diarrhea, nausea/vomiting, neutropenia/leukopenia, and neuropathy. We used linear model to AEL and logistic model to the other metrics, adjusting for age, sex, and number of metastases. Results: For nausea/vomiting, pts in Oxali-based chemo showed a higher AEL compared to patients in Iri-based (mean (M) [standard deviation (SD)] = 0.04 [0.06] vs. 0.02 [0.04]; p < 0.001) even though less pts had gr ≥ 3 (10.4% vs. 16.2%, resp.; p < 0.001), suggesting a lower-grade but persistent nausea/vomiting for Oxali-based. Among pts with diarrhea max gr 2, pts in Iri-based had higher AEL than with Oxali-based (M [SD] = 0.11 [0.11] vs. 0.08 [0.09]; p = 0.008), indicating more persistent diarrhea for Iri-based trts even among pts with same max grade. For neutropenia/leukopenia, pts in Oxali-based trts experienced higher AEL (M [SD] = 0.09 [0.13] vs. 0.05 [0.11], p < 0.001), and more pts in Oxali-based trts had gr ≥ 3 (70.1% vs. 64.4%; p = 0.002), suggesting that pts in Oxali-based trts experienced worst overall and max grade neutropenia/leukopenia compared to pts in Iri-based. Conclusions: To improve toxicity assessment in clinical trials, AEL and onset time of max grade are new measures that describe the evolution of adverse events over time. Further validation is warranted.

Abstract Background Relatively few studies have evaluated the role of host (germline) genetics in predicting outcome in diffuse large B-cell lymphoma (DLBCL), and of these, most were small candidate gene studies without replication and/or from the pre-R-CHOP era. We report the first genome-wide association study (GWAS) of outcome in DLBCL patients treated with rituximab and anthracycline-based chemotherapy. We used two independent studies for discovery, and validated the results in two additional independent studies. Methods In the discovery phase, we conducted a meta-analysis of two GWAS studies of DLBCL patients treated with rituximab and anthracycline-based chemotherapy, the first study as part of the prospective clinical trials of the LNH2003B program (N=540) of the Lymphoma Study Association (LYSA) and the second as part of the Molecular Epidemiology Resource (N=316), a prospective observational study from the University of Iowa/Mayo Clinic Lymphoma Specialized Program of Research Excellence (SPORE). Genotyping for the GWAS was performed using the Illumina 610 (LYSA) and 660 Quad (SPORE) BeadArrays (Illumina, San Diego, USA). HapMap2 was used to impute additional SNPs. Association testing was conducted assuming a log-additive genetic model and using Cox regression analyses. We calculated the hazard ratios (HRs) and 95% CIs for event-free survival (EFS), with adjustment for age, sex and age-adjusted IPI. The two studies were then combined using the fixed effects inverse variance method based on the beta estimates and standard errors from each study. From the meta-analysis, 89 SNPs were selected for validation, based on a minor allele frequency (MAF) >0.05 and LD pattern for multiple SNPs from the same region. Validation cohorts were an independent set of DLBCL patients from the SPORE (SPORE-2, N=366) and DLBCL patients from the prospective GOELAMS 075 phase III study (N=294), and validation genotyping was conducted using the Illumina VeraCode platform. We then conducted a meta-analysis of the 4 studies. We report SNPs with both a statistical significance of p<5x10-6 and effect sizes in the same direction across the 4 studies in the meta-analysis. For the latter SNPs, we also report the results for overall survival (OS), adjusted for age, sex and age-adjusted IPI. Results In the final meta-analysis of all four studies for the prediction of EFS, the top SNP was rs7765004 (MAF=0.30; HR=1.45; p=9.7 x 10-8) in the 6q21 region, which is near the MARCKS (myristoylated alanine-rich protein kinase C substrate) and HDAC2 genes. MARCKS encodes for a protein involved in cell mobility, while HDAC2 belongs to the histone deacetylase family. Additional SNPs from this region included rs6918103 (MAF=0.25; HR=1.38; p=7.6 x 10-6), which marks an EZH2 histone binding site, and rs11969684 (MAF=0.12; HR=1.43; p=3.3 x 10-5). The next hit was rs7712513 (MAF=0.31; HR=1.40; p=3.2 x 10-7), which is near arginine-fifty homeobox pseudogene 1 (ARGFXP1) on chromosome 5q23.2. This was followed by a region on chromosome 8q13.3, with the SNP rs9298183 (MAF=0.29; HR=1.41; p=1.4 x 10-6) from LOC100132891 showing the strongest association, along with 3 other SNPs from this region (rs2035376, rs6472637, and rs1600797; all p<5 x 10-6). These SNPs are near the musculin gene (MSC) a transcriptional repressor of E2A frequently silenced by promoter methylation in DLBCL. Finally, rs2253986 (MAF=0.27; HR=1.39; p=3.4 x 10-6) at chromosome 16q12.2, along with rs2253971 and rs6499810, were associated with EFS. These SNPs are located in CES5A (carboxylesterase 5A), which encodes a member of the carboxylesterase family. The family members are responsible for the hydrolysis or transesterification of various xenobiotics. The top SNPs associated with EFS also predicted OS, including rs7765004 (HR=1.41; p=1.2 x 10-5), rs7712513 (HR=1.39; p=8.4 x 10-6), rs9298183 (HR=1.36; p=3.5 x 10-5), and rs2253986 (HR=1.35; p=5.2 x 10-5). Conclusions Although our top SNPs on did not reach genome-wide significance (p<5x10-8), they showed clear consistency across the four large prospective studies and predicted both EFS and OS independently of IPI. The strongest findings were from loci on 6q21, 8q13.3, 5q23.2, and 16q12.2, and several genes from these regions are involved in methylation and xenobiotic metabolism, supporting a role for host genetic variation in these pathways in DLBCL prognosis. Disclosures: No relevant conflicts of interest to declare.

Abstract Background Immune editing is a major mechanism used by tumors to promote their survival. We have previously reported the presence of immunosuppressive monocytes (CD14+HLA-DR low/neg) in a number of cancers. Increased presence of these cells was associated with decreased treatment response and OS. Here we report an in vitro model to examine tumor monocyte cross talk and demonstrate that these monocytes may directly promote tumor cells’ resistance to chemotherapy. Method Monocytes from healthy donors were co-cultured with lymphoma cell lines (OCI-Ly1, OCI-Ly3, OCI-Ly7, OCI-Ly10, Su-DHL2, Su-DHL6, Jeko-1, Granta-519) with or without doxorubicin (DOX). Cultured cells were assessed for phenotype, viability, proliferation, and induced protein expression. Changes in vitro were confirmed in primary tissue by gene expression profile from 48 primary diffuse large B cell (DLBCL) lymphoma tumors. Results Monocytes converted CD14+HLA-DR+ to the CD14+HLA-DRlow/neg phenotype by co-culture with 5 of the 8 B cell lymphoma cell lines. The HLA-DR suppression was not due to the known regulatory effects of IL-10 nor was the effect seen in co-culture using B cells from healthy donors. Thus, the magnitude of DR loss is mediated by a yet undetermined mechanism. DOX incubation induced apoptosis in all of the cell lines. Co-culture with monocytes improved lymphoma cell survival in 3 of the 4 lymphoma cell lines. For example, untreated OCI-Ly3 had a 1.3±0.2 fold expansion that was reduced to 0.6±0.1 when treated with DOX (p<0.0001, n=13). However, when the cells were treated with DOX and co-cultured with monocytes the survival improved to 0.8±0.2 (p<0.0001, n=13). Having previously shown that CD14+HLA-DRlow/neg were capable of suppressing patient immunity, partially through arginase I, we determined if co-culture of monocytes with tumor cells increased arginase I expression in either monocytes or tumor cells. Not only did we see arginase I expression increases during co-culture(n=10, p<0.01), but surprisingly, the greatest increases were seen during co-culture and DOX treatment (n=10, p=0.01). To determine if our in vitro observations reflect human tumors, we used gene expression data obtained from 48 DLBCL tumors and found that increased CD14 gene expression was positively correlated with increased arginase I expression (n=48, p=0.02). Conclusions We have demonstrated that certain tumor cells induced phenotype changes in monocytes in an IL-10 independent and a tumor specific way. In those cells where tumors factors mediate monocyte phenotype, the monocytes can promote tumor cell resistance to cytotoxic killing by DOX, thus demonstrating a cross-talk between monocyte and tumor function. We found that in an environment of monocyte/tumor cross talk, arginase I expression is further increased in a cytotoxic environment. Together, this data demonstrates an active cross talk between monocytes and tumors resulting in multiple mechanisms of tumor resistance to chemotherapy. Disclosures: No relevant conflicts of interest to declare.

Abstract Background: Rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) therapy is standard of care for patients with newly diagnosed DLBCL. A majority of patients respond to R-CHOP, however ∼9% will have primary refractory disease and outcomes in these patients are extremely poor. While there is some knowledge of the pharmacogenomic (PGx) determinants of individual agents within R-CHOP, little is known about the PGx determinants of combination therapy. Our study aims to elucidate germline PGx determinants of drug bioavailability and undesirable side chain reactions in DLBCL patients treated with R-CHOP having potential to influence refractory disease. Methods: Newly diagnosed, DLBCL patients treated with R-CHOP between 2002 and 2012 with germline DNA and genotyping data were selected from the Molecular Epidemiology Resource within the Iowa/Mayo Lymphoma SPORE. Refractory disease was defined as either i) a best response of stable or progressive disease or ii) progressive disease by the end of the planned number of cycles of R-CHOP. PGx candidates within known pharmacokinetics and pharmacodynamics (PD) pathways for 11 genes (1,040 single nucleotide polymorphisms (SNPs)) were selected for study (CPT 2012;92(4):414-7). Odds ratios (ORs) and 95% confidence intervals (CI) were used to estimate the association of SNPs with refractory disease, controlling for age, gender, and age-adjusted International Prognostic Index. Results: The median age at diagnosis of 424 of DLBCL patients treated with R-CHOP was 62 years (range 18-92); 45.7% were female; and 58.4% had Ann Arbor Stage III-IV. By the end of initial therapy 40 patients had primary refractory disease. Of the 1,040 SNPs evaluated, 120 (12%) were significantly associated with refractory disease at the 5% significance threshold suggesting novel associations with drug metabolism. The top SNP from each PGx candidate gene are shown in Table, with ALDH5A1 and CYP2B6 showing the most significant associations. Conclusions: While needing replication, we identified associations within two PGx candidate genes that are potentially functionally related to cyclophosphamide PD: alleles within CYP2B6 may have relevance in R-CHOP outcomes due to potential competition with other drug metabolizing enzymes shared with prednisone and vincristine biotransformation in the liver, and alleles withinALDH5A1 may impact a deactivating side chain reaction in B lymphocytes. Table: R-CHOP PGx candidates with known side chain reactionsPGx candidaten SNPsTop SNPChrOR* (95% CI)P-valueMAFABCB1365rs3480093571.86 (1.14 – 3.04)1.32E-020.29ADH1A32rs122996841.66 (0.60 - 4.53)3.27E-010.038ALDH3A116rs2228100171.26 (0.71 - 2.22)4.28E-010.24ALDH5A1192rs946103466.30 (2.43 - 16.4)1.55E-040.030CBR110rs2835266212.10 (0.90 - 4.89)8.76E-020.052CBR356rs45527331212.12 (0.91 - 4.98)8.29E-020.050CYP2B6107rs1038376192.38 (1.44 - 3.94)7.74E-040.27CYP3A421rs2898858372.32 (0.80 - 6.78)1.23E-010.028CYP3A531rs778032871.69 (0.68 - 4.16)2.57E-010.054SLC22A16193rs11217403561.80 (1.06 - 3.03)2.78E-020.21SOD116rs1041740211.35 (0.83 - 2.19)2.26E-010.32Chr=chromosome, MAF=minor allele frequency, *Adjusted for age, sex, and age-adjusted International Prognostic Index Citation Format: Matthew K. Breitenstein, Susan L. Slager, Anne J. Novak, Matthew J. Maurer, Carrie A. Thompson, Umar Farooq, Brian K. Link, Thomas M. Habermann, Curtis L. Olswold, Andrew L. Feldman, Grzegorz S. Nowakowski, Stephen M. Ansell, Thomas E. Witzig, Richard M. Weinshilboum, Liewei Wang, James R. Cerhan. Pharmacogenomic of refractory response to R-CHOP therapy in diffuse large B-cell lymphoma (DLBCL) patients. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2015 Nov 5-9; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2015;14(12 Suppl 2):Abstract nr B143.

Abstract Chronic obstructive pulmonary disease (COPD) is a strong risk factor for lung cancer. Published studies about variations of genes encoding glutathione metabolism, DNA repair, and inflammatory response pathways in susceptibility to COPD were inconclusive. We evaluated 470 single-nucleotide polymorphisms (SNP) from 56 genes of these three pathways in 620 cases and 893 controls to identify susceptibility markers for COPD risk, using existing resources. We assessed SNP- and gene-level effects adjusting for sex, age, and smoking status. Differential genetic effects on disease risk with and without lung cancer were also assessed; cumulative risk models were established. Twenty-one SNPs were found to be significantly associated with risk of COPD (P &lt; 0.01); gene-based analyses confirmed two genes (GCLC and GSS) and identified three additional genes (GSTO2, ERCC1, and RRM1). Carrying 12 high-risk alleles may increase risk by 2.7-fold; eight SNPs altered COPD risk without lung cancer by 3.1-fold and 4 SNPs altered the risk with lung cancer by 2.3-fold. Our findings indicate that multiple genetic variations in the three selected pathways contribute to COPD risk through GCLC, GSS, GSTO2, ERCC1, and RRM1 genes. Functional studies are needed to elucidate the mechanisms of these genes in the development of COPD, lung cancer, or both. Cancer Prev Res; 5(3); 365–73. ©2011 AACR.